Chai Luyu, Zhou Yuxiang, Zhou Nan, Xiao Yao, Pang Renqi
Hainan Lecheng Institute of Real World Study, Qionghai, Hainan Province, China.
Rehabilitation Department, Zhoushan Guanghua Hospital, Zhoushan, Zhejiang Province, China.
PLoS One. 2025 Sep 8;20(9):e0328662. doi: 10.1371/journal.pone.0328662. eCollection 2025.
Acute kidney injury (AKI) in patients with liver cirrhosis represents a significant clinical challenge with high mortality rates. This study aimed to develop and validate a machine learning-based prediction model for 28-day mortality in AKI patients with liver cirrhosis using the MIMIC-IV database.
This retrospective study analyzed data from 4,168 AKI patients, including 601 with concurrent liver cirrhosis, from the MIMIC-IV database. Patient selection followed strict inclusion and exclusion criteria. The study implemented comprehensive data preprocessing, including feature normalization and selection through Recursive Feature Elimination. Multiple machine learning algorithms were evaluated, with model performance assessed through ROC curves, calibration curves, and precision-recall analysis. SHAP analysis was conducted to interpret feature contributions to mortality prediction.
The liver cirrhosis group demonstrated distinct clinical characteristics, including significantly lower age (median 60 vs 70 years, p < 0.001) and higher disease severity scores (SOFA 11 vs 8 points) compared to non-cirrhotic patients. Survival analysis confirmed significantly lower 28-day survival probability in the cirrhosis group (Log-rank test, χ2 = 46.5, p < 0.001). The Random Forest model achieved optimal performance with an AUC of 0.85 and precision-recall area of 0.81. SHAP analysis identified pH, anion gap, and total CO2 as the most significant predictive factors, with notable interaction effects among these indicators.
This study successfully developed a machine learning model for predicting 28-day mortality in AKI patients with liver cirrhosis. The model demonstrated superior clinical decision-making value compared to traditional scoring systems, particularly in moderate-risk threshold intervals. The findings emphasize the crucial role of acid-base balance indicators in mortality risk assessment, providing valuable insights for clinical intervention strategies.
肝硬化患者的急性肾损伤（AKI）是一项具有高死亡率的重大临床挑战。本研究旨在利用MIMIC-IV数据库开发并验证一种基于机器学习的肝硬化AKI患者28天死亡率预测模型。
这项回顾性研究分析了MIMIC-IV数据库中4168例AKI患者的数据，其中601例合并肝硬化。患者选择遵循严格的纳入和排除标准。该研究实施了全面的数据预处理，包括通过递归特征消除进行特征归一化和选择。评估了多种机器学习算法，通过ROC曲线、校准曲线和精确召回分析评估模型性能。进行SHAP分析以解释特征对死亡率预测的贡献。
与非肝硬化患者相比，肝硬化组表现出不同的临床特征，包括年龄显著更低（中位数60岁对70岁，p<0.001）和疾病严重程度评分更高（序贯器官衰竭评估[SOFA]11分对8分）。生存分析证实肝硬化组28天生存概率显著更低（对数秩检验，χ2=46.5，p<0.001）。随机森林模型表现最佳，AUC为0.85，精确召回面积为0.81。SHAP分析确定pH值、阴离子间隙和总二氧化碳为最显著的预测因素，这些指标之间存在显著的交互作用。
本研究成功开发了一种用于预测肝硬化AKI患者28天死亡率的机器学习模型。与传统评分系统相比，该模型具有更高的临床决策价值，尤其是在中度风险阈值区间。研究结果强调了酸碱平衡指标在死亡风险评估中的关键作用，为临床干预策略提供了有价值的见解。
